{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f7be389d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as mcolors\n",
    "from IPython.display import display_html\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "from sklearn.model_selection import cross_val_score,GridSearchCV,train_test_split\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score,confusion_matrix\n",
    "\n",
    "warnings.filterwarnings(\"ignore\", category=UserWarning)\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a2686c50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>...</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3863</th>\n",
       "      <td>0365-BZUWY</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>17</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>102.55</td>\n",
       "      <td>1742.5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      customerID gender  SeniorCitizen Partner Dependents  tenure  \\\n",
       "3863  0365-BZUWY   Male              0     Yes         No      17   \n",
       "\n",
       "     PhoneService MultipleLines InternetService OnlineSecurity  ...  \\\n",
       "3863          Yes           Yes     Fiber optic             No  ...   \n",
       "\n",
       "     DeviceProtection TechSupport StreamingTV StreamingMovies        Contract  \\\n",
       "3863              Yes          No         Yes             Yes  Month-to-month   \n",
       "\n",
       "     PaperlessBilling     PaymentMethod MonthlyCharges  TotalCharges Churn  \n",
       "3863              Yes  Electronic check         102.55        1742.5    No  \n",
       "\n",
       "[1 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(r\"C:\\Users\\DD\\Desktop\\WA_Fn-UseC_-Telco-Customer-Churn.csv\")\n",
    "data.shape\n",
    "data.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1b7fda36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['用户ID', '性别', '是否有配偶', '是否经济独立', '是否开通电话业务', '是否开通多条电话业务', '是否开通互联网服务',\n",
       "       '是否开通网络安全服务', '是否开通在线备份服务', '是否开通设备保护服务', '是否开通技术支持业务', '是否开通网络电视',\n",
       "       '是否开通网络电影', '合同签订方式', '是否开通电子账单', '付款方式', '总费用', '是否流失'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns= ['用户ID','性别','是否老年人','是否有配偶','是否经济独立','用户入网时间','是否开通电话业务','是否开通多条电话业务',\n",
    "               '是否开通互联网服务','是否开通网络安全服务','是否开通在线备份服务','是否开通设备保护服务','是否开通技术支持业务',\n",
    "               '是否开通网络电视','是否开通网络电影','合同签订方式','是否开通电子账单','付款方式','月度费用','总费用','是否流失']\n",
    "# data.info()\n",
    "data.select_dtypes('object').columns   # 查看为object对象的字段有哪些"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "af1919b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_index(data_col, val):\n",
    "    \"\"\"\n",
    "    查询某值在某列中第一次出现位置的索引，没有则返回-1\n",
    "    :param data_col: 查询的列\n",
    "    :param val: 具体取值\n",
    "    \"\"\"\n",
    "    val_list = [val]\n",
    "    if data_col.isin(val_list).sum() == 0:\n",
    "        index = -1\n",
    "    else:\n",
    "        index = data_col.isin(val_list).idxmax()\n",
    "    return index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fb14842e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "488\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "485    6130.85\n",
       "486       1415\n",
       "487    6201.95\n",
       "488           \n",
       "489      74.35\n",
       "Name: 总费用, dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(find_index(data['总费用'], ' '))\n",
    "data[\"总费用\"][485:490]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8952ce5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 单个修改\n",
    "# data.loc[488,\"总费用\"] = data.loc[488,\"月度费用\"] * data.loc[488,\"用户入网时间\"].astype('int64')\n",
    "\n",
    "# 定义函数进行数据转换，异常的就转换为0\n",
    "def convert_to_float(val):\n",
    "    try:\n",
    "        return np.float64(val)\n",
    "    except ValueError:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1333696e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>用户ID</th>\n",
       "      <th>性别</th>\n",
       "      <th>是否老年人</th>\n",
       "      <th>是否有配偶</th>\n",
       "      <th>是否经济独立</th>\n",
       "      <th>用户入网时间</th>\n",
       "      <th>是否开通电话业务</th>\n",
       "      <th>是否开通多条电话业务</th>\n",
       "      <th>是否开通互联网服务</th>\n",
       "      <th>是否开通网络安全服务</th>\n",
       "      <th>...</th>\n",
       "      <th>是否开通设备保护服务</th>\n",
       "      <th>是否开通技术支持业务</th>\n",
       "      <th>是否开通网络电视</th>\n",
       "      <th>是否开通网络电影</th>\n",
       "      <th>合同签订方式</th>\n",
       "      <th>是否开通电子账单</th>\n",
       "      <th>付款方式</th>\n",
       "      <th>月度费用</th>\n",
       "      <th>总费用</th>\n",
       "      <th>是否流失</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3267</th>\n",
       "      <td>8902-ZEOVF</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>47</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>...</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>20.05</td>\n",
       "      <td>951.55</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            用户ID    性别  是否老年人 是否有配偶 是否经济独立  用户入网时间 是否开通电话业务 是否开通多条电话业务  \\\n",
       "3267  8902-ZEOVF  Male      0   Yes    Yes      47      Yes         No   \n",
       "\n",
       "     是否开通互联网服务           是否开通网络安全服务  ...           是否开通设备保护服务  \\\n",
       "3267        No  No internet service  ...  No internet service   \n",
       "\n",
       "               是否开通技术支持业务             是否开通网络电视             是否开通网络电影    合同签订方式  \\\n",
       "3267  No internet service  No internet service  No internet service  One year   \n",
       "\n",
       "     是否开通电子账单          付款方式   月度费用     总费用  是否流失  \n",
       "3267       No  Mailed check  20.05  951.55    No  \n",
       "\n",
       "[1 rows x 21 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['总费用'] = data['总费用'].apply(convert_to_float)\n",
    "data.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1c203947",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>是否老年人</th>\n",
       "      <th>用户入网时间</th>\n",
       "      <th>月度费用</th>\n",
       "      <th>总费用</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.162147</td>\n",
       "      <td>32.371149</td>\n",
       "      <td>64.761692</td>\n",
       "      <td>2279.734304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.368612</td>\n",
       "      <td>24.559481</td>\n",
       "      <td>30.090047</td>\n",
       "      <td>2266.794470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.250000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>35.500000</td>\n",
       "      <td>398.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>70.350000</td>\n",
       "      <td>1394.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>89.850000</td>\n",
       "      <td>3786.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>118.750000</td>\n",
       "      <td>8684.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             是否老年人       用户入网时间         月度费用          总费用\n",
       "count  7043.000000  7043.000000  7043.000000  7043.000000\n",
       "mean      0.162147    32.371149    64.761692  2279.734304\n",
       "std       0.368612    24.559481    30.090047  2266.794470\n",
       "min       0.000000     0.000000    18.250000     0.000000\n",
       "25%       0.000000     9.000000    35.500000   398.550000\n",
       "50%       0.000000    29.000000    70.350000  1394.550000\n",
       "75%       0.000000    55.000000    89.850000  3786.600000\n",
       "max       1.000000    72.000000   118.750000  8684.800000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a5fa7964",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"用户ID\"].value_counts().sum()\n",
    "data['用户ID'].nunique() == data.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f5a152ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_unique(df, li):\n",
    "    _ = {}\n",
    "    for i in li:\n",
    "        if i.endswith('N'):\n",
    "            value = [df[i[:-1]].nunique()]\n",
    "        else:\n",
    "            value = df[i].unique()\n",
    "        _[i] = value\n",
    "    max_length = max(len(value) for value in _.values())\n",
    "    for key, value in _.items():\n",
    "        if len(value) < max_length:\n",
    "            _[key] = list(value) + [' '] * (max_length - len(value))\n",
    "    return pd.DataFrame(_)\n",
    "li = ['用户IDN','性别','是否老年人','是否有配偶','是否经济独立','用户入网时间','是否开通电话业务','是否开通多条电话业务',\n",
    "      '是否开通互联网服务','是否开通网络安全服务','是否开通在线备份服务','是否开通设备保护服务','是否开通技术支持业务',\n",
    "      '是否开通网络电视','是否开通网络电影','合同签订方式','是否开通电子账单','付款方式','月度费用','总费用','是否流失']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "47338222",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1 34  2 45  8 22 10 28 62 13 16 58 49 25 69 52 71 21 12 30 47 72 17 27\n",
      "  5 46 11 70 63 43 15 60 18 66  9  3 31 50 64 56  7 42 35 48 29 65 38 68\n",
      " 32 55 37 36 41  6  4 33 67 23 57 61 14 20 53 40 59 24 44 19 54 51 26  0\n",
      " 39]\n"
     ]
    }
   ],
   "source": [
    "show_unique(data, li).head()\n",
    "print(data['用户入网时间'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e50d6fee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7043, 46)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 数据备份，剔除用户ID列【第一列】\n",
    "df = data.iloc[:,1:].copy()\n",
    " \n",
    "# 将标签Yes-No 转化为 1-0 【手动转换】\n",
    "df['是否流失'].replace(to_replace='Yes', value=1, inplace=True)\n",
    "df['是否流失'].replace(to_replace='No',  value=0, inplace=True)\n",
    "\n",
    "# 其他所有分类变量转化为哑变量(将分类变量转换为数值变量)，连续变量保留不变\n",
    "df_dummies = pd.get_dummies(df)\n",
    "df_dummies.sample(5)\n",
    "df_dummies[['是否流失', '用户入网时间', '月度费用', '总费用']].T\n",
    "df_dummies.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "69fbbf53",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 离散字段\n",
    "category_cols = ['用户ID','性别','是否老年人','是否有配偶','是否经济独立','是否开通电话业务','是否开通多条电话业务',\n",
    "               '是否开通互联网服务','是否开通网络安全服务','是否开通在线备份服务','是否开通设备保护服务','是否开通技术支持业务',\n",
    "               '是否开通网络电视','是否开通网络电影','合同签订方式','是否开通电子账单','付款方式',]\n",
    " \n",
    "# 连续字段\n",
    "numeric_cols = ['用户入网时间','月度费用', '总费用']\n",
    " \n",
    "# 标签字段\n",
    "target = '是否流失'\n",
    " \n",
    "# 字段太多断言看看验证是否划分完全\n",
    "assert len(category_cols) + len(numeric_cols) + 1 == data.shape[1]  # 加 1 表示‘是否流失’"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2717fed6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最佳超参数组合为: {'min_samples_leaf': 2, 'min_samples_split': 10, 'n_estimators': 50}\n",
      "0.797971602434077\n",
      "[0.83975659 0.79716024 0.82150101 0.78904665 0.77484787 0.80324544\n",
      " 0.77890467 0.80933063 0.77484787 0.79107505]\n"
     ]
    }
   ],
   "source": [
    "# 划分特征和目标变量\n",
    "X = df_dummies.drop('是否流失', axis=1)\n",
    "y = df_dummies['是否流失']\n",
    "\n",
    "# 划分训练集和测试集，这里测试集占比设为0.3（可根据实际情况调整）\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "\n",
    "# 定义要调优的超参数范围\n",
    "param_grid = {\n",
    "    'n_estimators': [50, 100, 150], # 代表森林中决策树的数量\n",
    "    'min_samples_split': [2, 5, 10], # 在一个节点（树的节点）至少需要多少个样本才能继续进行分裂\n",
    "    'min_samples_leaf': [1, 2, 4] # 每个叶节点（树的末端节点）最少需要包含的样本数\n",
    "}\n",
    "\n",
    "# 创建随机森林分类器对象\n",
    "alg = RandomForestClassifier(random_state=15)\n",
    "\n",
    "# 使用GridSearchCV进行超参数调优,指定交叉验证的折数cv\n",
    "grid_search = GridSearchCV(alg, param_grid, cv=5)\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "# 输出最佳超参数组合\n",
    "print(\"最佳超参数组合为:\", grid_search.best_params_)\n",
    "\n",
    "# 使用最佳超参数组合重新训练模型\n",
    "best_alg = grid_search.best_estimator_\n",
    "best_alg.fit(X_train, y_train)\n",
    "\n",
    "# 在训练集上进行交叉验证\n",
    "scores = cross_val_score(best_alg, X_train, y_train, cv=10)\n",
    "print(scores.mean())\n",
    "print(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "43cebcbd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "在随机森林的准确率: 0.7988641741599621\n",
      "在随机森林的精确率: 0.6744730679156908\n",
      "在随机森林的召回率: 0.5017421602787456\n",
      "在随机森林的F1值: 0.5754245754245754\n"
     ]
    }
   ],
   "source": [
    "# 在测试集上进行预测并计算评估指标\n",
    "y_pred = best_alg.predict(X_test)\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision = precision_score(y_test, y_pred)\n",
    "recall = recall_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "\n",
    "print(\"在随机森林的准确率:\", accuracy)\n",
    "print(\"在随机森林的精确率:\", precision)\n",
    "print(\"在随机森林的召回率:\", recall)\n",
    "print(\"在随机森林的F1值:\", f1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0d4e237b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 960x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 使用最佳超参数组合重新训练模型\n",
    "best_alg = grid_search.best_estimator_\n",
    "best_alg.fit(X_train, y_train)\n",
    "\n",
    "# 在测试集上进行预测\n",
    "y_pred = best_alg.predict(X_test)\n",
    "\n",
    "# 计算混淆矩阵\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "# 可视化混淆矩阵\n",
    "plt.figure(figsize=(8, 6),dpi=120)\n",
    "sns.heatmap(conf_matrix, annot=True, fmt=\"d\", cmap=\"Purples\",\n",
    "            xticklabels=[\"未流失\", \"流失\"], yticklabels=[\"未流失\", \"流失\"])\n",
    "plt.xlabel('预测标签')\n",
    "plt.ylabel('真实标签')\n",
    "plt.title('随机森林矩阵')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5e0aeab2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC值为: 0.8511143486539293\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, roc_auc_score\n",
    "# 在测试集上获取预测概率\n",
    "y_pred_proba = best_alg.predict_proba(X_test)[:, 1]\n",
    "\n",
    "# 计算AUC值\n",
    "auc_score = roc_auc_score(y_test, y_pred_proba)\n",
    "print(\"AUC值为:\", auc_score)\n",
    "\n",
    "# 计算ROC曲线的相关数据\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_pred_proba)\n",
    "\n",
    "# 绘制ROC曲线\n",
    "plt.figure(figsize=(8, 6),dpi=100)\n",
    "plt.plot(fpr, tpr,color='#9F79EE')\n",
    "plt.xlabel('假阳性率')\n",
    "plt.ylabel('真阳性率')\n",
    "plt.title('ROC曲线情况')\n",
    "plt.plot([0, 1], [0, 1], linestyle='--', color='gray')   # 绘制对角线（表示随机猜测的情况）\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d23a813f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, confusion_matrix\n",
    "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "# 对分类变量进行编码\n",
    "categorical_cols = X.select_dtypes(include=['object']).columns  # 选择所有分类变量\n",
    "le = LabelEncoder()\n",
    "X[categorical_cols] = X[categorical_cols].apply(lambda x: le.fit_transform(x))\n",
    "\n",
    "# 划分训练集和测试集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c2ee5cdf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "逻辑回归模型精准度 - 准确率: 80.97%, 精确率: 67.84%, 召回率: 56.97%, F1值: 61.93%, AUC值: 85.82%\n"
     ]
    }
   ],
   "source": [
    "# 对分类变量进行编码\n",
    "categorical_cols = X.select_dtypes(include=['object']).columns  # 选择所有分类变量\n",
    "le = LabelEncoder()\n",
    "X[categorical_cols] = X[categorical_cols].apply(lambda x: le.fit_transform(x))\n",
    "\n",
    "# 划分训练集和测试集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "\n",
    "# 逻辑回归模型\n",
    "logistic_model = LogisticRegression(random_state=0, solver='lbfgs')\n",
    "logistic_model.fit(X_train, y_train)\n",
    "\n",
    "# 预测结果\n",
    "y_pred_logistic = logistic_model.predict(X_test)\n",
    "\n",
    "# 模型精准度\n",
    "accuracy_logistic = accuracy_score(y_test, y_pred_logistic)\n",
    "precision_logistic = precision_score(y_test, y_pred_logistic)\n",
    "recall_logistic = recall_score(y_test, y_pred_logistic)\n",
    "f1_logistic = f1_score(y_test, y_pred_logistic)\n",
    "roc_auc_logistic = roc_auc_score(y_test, logistic_model.predict_proba(X_test)[:, 1])\n",
    "\n",
    "print(\"逻辑回归模型精准度 - 准确率: {:.2f}%, 精确率: {:.2f}%, 召回率: {:.2f}%, F1值: {:.2f}%, AUC值: {:.2f}%\".format(\n",
    "    accuracy_logistic * 100, precision_logistic * 100, recall_logistic * 100, f1_logistic * 100, roc_auc_logistic * 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "90b3d1b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "梯度提升树模型精准度 - 准确率: 80.74%, 精确率: 68.68%, 召回率: 53.48%, F1值: 60.14%, AUC值: 85.78%\n"
     ]
    }
   ],
   "source": [
    "# 梯度提升树模型\n",
    "gbdt_model = GradientBoostingClassifier(random_state=0)\n",
    "gbdt_model.fit(X_train, y_train)\n",
    "\n",
    "# 预测结果\n",
    "y_pred_gbdt = gbdt_model.predict(X_test)\n",
    "\n",
    "# 模型精准度\n",
    "accuracy_gbdt = accuracy_score(y_test, y_pred_gbdt)\n",
    "precision_gbdt = precision_score(y_test, y_pred_gbdt)\n",
    "recall_gbdt = recall_score(y_test, y_pred_gbdt)\n",
    "f1_gbdt = f1_score(y_test, y_pred_gbdt)\n",
    "roc_auc_gbdt = roc_auc_score(y_test, gbdt_model.predict_proba(X_test)[:, 1])\n",
    "\n",
    "print(\"梯度提升树模型精准度 - 准确率: {:.2f}%, 精确率: {:.2f}%, 召回率: {:.2f}%, F1值: {:.2f}%, AUC值: {:.2f}%\".format(\n",
    "    accuracy_gbdt * 100, precision_gbdt * 100, recall_gbdt * 100, f1_gbdt * 100, roc_auc_gbdt * 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e1544d24",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBoost模型精准度 - 准确率: 78.51%, 精确率: 62.82%, 召回率: 51.22%, F1值: 56.43%, AUC值: 83.38%\n"
     ]
    }
   ],
   "source": [
    "# XGBoost模型\n",
    "xgb_model = XGBClassifier(use_label_encoder=False, eval_metric='logloss')\n",
    "xgb_model.fit(X_train, y_train)\n",
    "\n",
    "# 预测结果\n",
    "y_pred_xgb = xgb_model.predict(X_test)\n",
    "\n",
    "# 模型精准度\n",
    "accuracy_xgb = accuracy_score(y_test, y_pred_xgb)\n",
    "precision_xgb = precision_score(y_test, y_pred_xgb)\n",
    "recall_xgb = recall_score(y_test, y_pred_xgb)\n",
    "f1_xgb = f1_score(y_test, y_pred_xgb)\n",
    "roc_auc_xgb = roc_auc_score(y_test, xgb_model.predict_proba(X_test)[:, 1])\n",
    "\n",
    "print(\"XGBoost模型精准度 - 准确率: {:.2f}%, 精确率: {:.2f}%, 召回率: {:.2f}%, F1值: {:.2f}%, AUC值: {:.2f}%\".format(\n",
    "    accuracy_xgb * 100, precision_xgb * 100, recall_xgb * 100, f1_xgb * 100, roc_auc_xgb * 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d92a8202",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 混淆矩阵\n",
    "conf_matrix_logistic = confusion_matrix(y_test, y_pred_logistic)\n",
    "conf_matrix_gbdt = confusion_matrix(y_test, y_pred_gbdt)\n",
    "conf_matrix_xgb = confusion_matrix(y_test, y_pred_xgb)\n",
    "\n",
    "# 可视化混淆矩阵\n",
    "plt.figure(figsize=(15, 5))\n",
    "plt.subplot(1, 3, 1)\n",
    "sns.heatmap(conf_matrix_logistic, annot=True, fmt='d', cmap='Blues', xticklabels=['未流失', '流失'], yticklabels=['未流失', '流失'])\n",
    "plt.title('逻辑回归混淆矩阵')\n",
    "plt.subplot(1, 3, 2)\n",
    "sns.heatmap(conf_matrix_gbdt, annot=True, fmt='d', cmap='Blues', xticklabels=['未流失', '流失'], yticklabels=['未流失', '流失'])\n",
    "plt.title('梯度提升树混淆矩阵')\n",
    "plt.subplot(1, 3, 3)\n",
    "sns.heatmap(conf_matrix_xgb, annot=True, fmt='d', cmap='Blues', xticklabels=['未流失', '流失'], yticklabels=['未流失', '流失'])\n",
    "plt.title('XGBoost混淆矩阵')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "50a1f771",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, roc_auc_score\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 计算逻辑回归模型的ROC曲线和AUC值\n",
    "fpr_logistic, tpr_logistic, _ = roc_curve(y_test, logistic_model.predict_proba(X_test)[:, 1])\n",
    "roc_auc_logistic = roc_auc_score(y_test, logistic_model.predict_proba(X_test)[:, 1])\n",
    "\n",
    "# 绘制逻辑回归的ROC曲线\n",
    "plt.figure(figsize=(6, 5))\n",
    "plt.plot(fpr_logistic, tpr_logistic, label=f'逻辑回归 (AUC = {roc_auc_logistic:.2f})', color='blue')\n",
    "plt.plot([0, 1], [0, 1], 'r--')  # 对角线，表示随机猜测的性能\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.0])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('逻辑回归 ROC曲线')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()\n",
    "\n",
    "# 计算梯度提升树模型的ROC曲线和AUC值\n",
    "fpr_gbdt, tpr_gbdt, _ = roc_curve(y_test, gbdt_model.predict_proba(X_test)[:, 1])\n",
    "roc_auc_gbdt = roc_auc_score(y_test, gbdt_model.predict_proba(X_test)[:, 1])\n",
    "\n",
    "# 绘制梯度提升树的ROC曲线\n",
    "plt.figure(figsize=(6, 5))\n",
    "plt.plot(fpr_gbdt, tpr_gbdt, label=f'梯度提升树 (AUC = {roc_auc_gbdt:.2f})', color='green')\n",
    "plt.plot([0, 1], [0, 1], 'r--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.0])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('梯度提升树 ROC曲线')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()\n",
    "\n",
    "# 计算XGBoost模型的ROC曲线和AUC值\n",
    "fpr_xgb, tpr_xgb, _ = roc_curve(y_test, xgb_model.predict_proba(X_test)[:, 1])\n",
    "roc_auc_xgb = roc_auc_score(y_test, xgb_model.predict_proba(X_test)[:, 1])\n",
    "\n",
    "# 绘制XGBoost的ROC曲线\n",
    "plt.figure(figsize=(6, 5))\n",
    "plt.plot(fpr_xgb, tpr_xgb, label=f'XGBoost (AUC = {roc_auc_xgb:.2f})', color='red')\n",
    "plt.plot([0, 1], [0, 1], 'r--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.0])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('XGBoost ROC曲线')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0562ea19",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "575f643f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
